Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features
<italic>Goal:</italic> Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies–a time-consuming, laborious task t...
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IEEE
2021-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| author | Tomer Czyzewski Nati Daniel Mark Rochman Julie Caldwell Garrett Osswald Margaret Collins Marc Rothenberg Yonatan Savir |
| author_facet | Tomer Czyzewski Nati Daniel Mark Rochman Julie Caldwell Garrett Osswald Margaret Collins Marc Rothenberg Yonatan Savir |
| author_sort | Tomer Czyzewski |
| collection | DOAJ |
| description | <italic>Goal:</italic> Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies–a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. <italic>Results:</italic> In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. <italic>Conclusions:</italic> We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics. |
| format | Article |
| id | doaj-art-0811d8686b0d4e79a1e924565ba7e174 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-0811d8686b0d4e79a1e924565ba7e1742025-08-20T03:32:40ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762021-01-01221822310.1109/OJEMB.2021.30895529457060Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global featuresTomer Czyzewski0Nati Daniel1https://orcid.org/0000-0002-0939-3379Mark Rochman2Julie Caldwell3Garrett Osswald4Margaret Collins5Marc Rothenberg6Yonatan Savir7https://orcid.org/0000-0002-5345-8491Department of Physiology, Biophysics, and System Biology, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, IsraelDepartment of Physiology, Biophysics, and System Biology, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, IsraelDivision of Allergy, and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USADivision of Allergy, and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USADivision of Allergy, and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USADivision of Pathology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USADivision of Allergy, and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USADepartment of Physiology, Biophysics, and System Biology, Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel<italic>Goal:</italic> Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies–a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. <italic>Results:</italic> In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. <italic>Conclusions:</italic> We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.https://ieeexplore.ieee.org/document/9457060/Decision support systemdeep convolutional networkdigital pathologyeosinophilic esophagitissmall features detection |
| spellingShingle | Tomer Czyzewski Nati Daniel Mark Rochman Julie Caldwell Garrett Osswald Margaret Collins Marc Rothenberg Yonatan Savir Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features IEEE Open Journal of Engineering in Medicine and Biology Decision support system deep convolutional network digital pathology eosinophilic esophagitis small features detection |
| title | Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features |
| title_full | Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features |
| title_fullStr | Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features |
| title_full_unstemmed | Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features |
| title_short | Machine Learning Approach for Biopsy-Based Identification of Eosinophilic Esophagitis Reveals Importance of Global features |
| title_sort | machine learning approach for biopsy based identification of eosinophilic esophagitis reveals importance of global features |
| topic | Decision support system deep convolutional network digital pathology eosinophilic esophagitis small features detection |
| url | https://ieeexplore.ieee.org/document/9457060/ |
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